The impact of semantics on aspect level opinion mining

Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products' comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects' synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure.

Automated summary

In recent years, many users prefer online shopping, allowing customers to submit comments and feedback on shopping websites. Opinion mining and sentiment analysis are used to assist buyers and sellers in making purchase decisions. A semantic-based aspect level opinion mining (SALOM) model is proposed to consider negation words and other types of product aspects, with promising experimental results.